The invention relates to signal processing and, more specifically, to a processor and processing methods designed to suppress interference in an input signal and, hence, to decrease degradation of a signal of interest caused by the presence of such interference.
The invention is especially applicable to spread spectrum communications signal processors or to other situations where the signal of interest is to be detected by a correlation process and the interference (a) is much stronger than the signal of interest, and (b) can be characterized by greater predictability than simple white Gaussian (random) noise.
In a single-input communications signal processor, the input signal typically is sampled and the output signal is synthesized at discrete times. At any time, the output signal is then some function of the input signal at such discrete times with the function or transformation defining the signal processing. (Herein function, transformation and mapping are used interchangeably to mean a mathematical relationship/correspondence between two sets, e.g., signal input and output. A function/transformation/mapping that is nonlinear may be termed a nonlinearity for short.)
The most common single-input signal processors for interference suppression are linear, i.e., the function determining the output can be expressed as a sum of the inputs at each sample time, multiplied by constants. Such a processor is simply a filter, and is effective at suppressing interference only if the normalized power spectrum (the power distribution by frequency of the interference is not equal to that of the signal of interest.
An additional limitation of some linear signal processors is that those that are designed to adapt to the interference environment usually do so by means of an iterative approach in which the results of the processing currently being performed are inspected and used to compute modifications to the signal processing parameters. Such iterative approaches can be slow to adapt and may adapt inappropriately under certain conditions.
Linear signal processors have been studied extensively and their properties are well known. A sampling of such processors can be found in U.S. Pat. Nos. 4,811,261 to Kobayashi et al; 4,243,935 to McCool et al; 4,207,624 to Dentino et al; 3,889,108 to Cantrell; and 3,867,712 to Harthill et al.
In some signal processors the function used to generate an output signal cannot be put into linear form. In general, such nonlinear processes are much less familiar than linear ones to those with ordinary skill in the art, and their effects are more difficult to predict. One of the best known types of nonlinearity is that having zero memory, that is, the output at time t depends only on the input at that same time. A brief description of two simple zero-memory nonlinearities will help clarify how such processing works.
The first type of nonlinearity is used in cases when the interference to be suppressed is impulsive, that is, it consists largely of isolated pulses. Examples are pulse jammers, motor vehicle ignition noise, and atmospherics.
FIG. 1a shows an input signal tainted with a pulse from such an interference source. As a specific example, the signal processor might be trying to decode a phase-keyed signal, to determine whether a data bit (FIG. 1b) or data bit 0 (FIG. 1c) was sent. The processor computes the correlation of the input signal with each of the test waveforms (data 0 and 1) and chooses the one whose correlation is the greatest. In the example shown, one can see that although the true signal of interest was the data 1 waveform, the interference, during its pulse, happens to correlate strongly with the data 0 waveform. As a result, if the interference pulse is strong enough, it will outweigh the signal of interest in the correlation sum and cause a received error.
If the interference environment is known in advance, the assumption can be made that the weak part of the waveform in FIG. 1a is the signal of interest and the strong part is the interference. To improve detection of the signal of interest, the processor could then disregard the high-level input signal samples since they are dominated by interference. This suggests using a zero-memory nonlinearity in which a cutoff threshold, A.sub.t, is set just above the maximum level of the desired signal of interest (dotted line in FIG. 1a). A.sub.x is the "envelope" amplitude of x (dashed line in FIG. 1a). When A.sub.x .gtoreq.A.sub.t the nonlinearity reduces the output to zero.
When this nonlinearity is applied to the input waveform of FIG. 1, it produces the output, FIG. 1d, which is then correlated against the data 0 and 1 waveforms, i.e., the output waveform is multiplied sample by sample by the test waveform and the resulting products are summed. Although some of the signal of interest is lost during the interference pulse, all of the interference is suppressed and the correct data decision will be made. This nonlinearity is called a "hole puncher," and is just one of many possible limiters used to reduce the impact of impulsive interference by de-emphasizing large-amplitude parts of an input waveform. See, e.g., U.S. Pat. No. 4,530,076 to Dwyer. A frequency-domain analog is described in U.S. Pat. No. 4,613,978 to Kurth et al.
A second zero-memory nonlinearity is that used against constant-amplitude interference. This interference has amplitude behavior that is just the opposite of impulsive interference, and suppressing it requires a very different nonlinearity. An input waveform is shown in FIG. 2a. It is dominated by an interference waveform with peak amplitude, A. A weak signal of interest, the same as in FIG. 1, is also present. The input signal of interest-plus-interference sum fluctuates, its peak amplitude, A.sub.x, surpassing A when signal of interest and interference are in phase and add constructively, and filling short of A when the signal of interest and interference are out of phase and tend to cancel. The correlation sum formed by the processor will indicate the wrong data if the interference is strong enough and out of phase with the signal of interest for a large enough fraction of the correlation period.
In the case of strong constant-amplitude interference, it is clear that when the input signal envelope is greater than A, the signal of interest is in phase with the interference and the input waveform can be used as an estimate of the signal of interest. Conversely, when the input signal envelope is less than A, the signal of interest must be out of phase with the interference and can be estimated as the negative of the input waveform. Moreover, the more the peak amplitude deviates from A, the more exactly the signal of interest must be in (or out of) phase with the interference and the better it is estimated as the input waveform (or its negative).
A reasonable nonlinearity to use against constant-amplitude interference might therefore produce an output with the same phase as that of the input, but with an amplitude proportional to the difference between A.sub.x and A. This process is sometimes called a "limiter/canceller". See, e.g., U.S. Pat. Nos. 4,710,723 to Pelchat et al; 4,270,223 to Marston; and 3,605,018 and 3,478,268 both to Coviello.
Note that unlike the linear processes, such as filters, and other techniques such as sine-wave cancellation (U.S. Pat. Nos. 4,349,916 to Roeder; 3,949,309 to Pecar; and 4,287,475 to Eaton et al), these nonlinear processes do not depend on any particular frequency characteristics on the part of the interference. For example, a limiter/canceller can greatly improve detection of a weak phase-keyed signal of interest in the presence of a much stronger constant-amplitude phase-keyed interference source, even though the interference power is distributed in frequency exactly the same as the signal of interest and therefore cannot be suppressed by single-input filtering.
However, "hole punchers"; limiter/cancellers; and other nonlinear techniques intended for use against specific interference types do not usually implement adaptive estimates of the interference of the moment as does the invention described and claimed herein. Previous adaptive nonlinear techniques (see, e.g., U.S. Pat. Nos. 4,792,915 to Adams et al; 4,774,682 to White; 4,530,076 to Dwyer; and 3,833,797 to Grobman et al) do not implement optimum signal detection transforms based on the full probability distribution of interference variables, and therefore do not suppress as broad a range of interference types as effectively as does the invention described and claimed herein. Further, the invention also does not need the multiple inputs found in, e.g., directional antenna combining (U.S. Pat. Nos. 4,355,368 to Zeidler et al and 4,017,859 to Medwin) or reference interference subtraction (No. 4,594,695 to Garconnat et al).